Enables online, incremental 3D Gaussian Splatting for thousands of frames by replacing global reprocessing with a causal, streaming update framework.
March 17, 2026
Original Paper
S2GS: Streaming Semantic Gaussian Splatting for Online Scene Understanding and Reconstruction
arXiv · 2603.14232
The Takeaway
Most 3DGS methods are offline and fail at around 80 frames due to rapid memory growth. S2GS scales to 1,000+ frames with significantly slower memory growth, enabling real-time semantic mapping and reconstruction for long-horizon robotics and AR applications.
From the abstract
Existing offline feed-forward methods for joint scene understanding and reconstruction on long image streams often repeatedly perform global computation over an ever-growing set of past observations, causing runtime and GPU memory to increase rapidly with sequence length and limiting scalability. We propose Streaming Semantic Gaussian Splatting (S2GS), a strictly causal, incremental 3D Gaussian semantic field framework: it does not leverage future frames and continuously updates scene geometry,